Table 2_Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.xlsx

BackgroundMachine learning (ML) models are increasingly utilized to predict mortality in patients with sepsis-associated acute kidney injury (SA-AKI), frequently surpassing traditional scoring systems. Despite their efficacy, inconsistencies in model quality remain a concern. This review aims to evaluate existing ML-based SA-AKI mortality prediction models, with a focus on development quality, methodological rigor, and predictive performance.
ObjectiveTo systematically assess ML-based mortality risk prediction models for SA-AKI patients.
MethodsA comprehensive literature search on ML-based SA-AKI mortality prediction models was conducted across PubMed, Cochrane, Embase, and Web of Science from the inception of these databases until July 2025. Two researchers independently screened the literature, extracted data, and assessed model quality employing the Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence.
ResultsNine studies were included, all of which entailed model development and validation phases; five were solely internally validated while four underwent external validation as well. The studies utilized 18 different algorithms, with Random Forest and Extreme Gradient Boosting being the most prevalent. The majority of the studies employed K-nearest neighbor or Multiple Imputation by Chained Equations for handling missing values and utilized Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta's algorithm for feature selection. Seven studies assessed model calibration performance. The Area Under the Curve (AUC) for the training sets generally ranged from 0.75 to 0.99, which decreased to 0.70 to 0.87 during internal validation. Extreme Gradient Boosting consistently showed robust performance in external validation. The final predictors encompassed six principal categories: demographic information, vital signs, laboratory tests, disease severity, comorbidities, and interventions.
ConclusionsML models demonstrate promising performance and applicability in predicting mortality risk in SA-AKI patients, with consistent core predictors. Nevertheless, most studies exhibit a potential risk of bias. Future efforts should aim to enhance the standardization of data processing, feature selection, and validation processes. Additionally, there is a need to focus on the construction of prospective models based on early variables, and to ensure the interpretability and clinical integration of the models to facilitate their practical application in healthcare workflows.
Systematic review registrationidentifier: CRD42025634551.

【背景】机器学习（Machine Learning, ML）模型正愈发广泛地被用于预测脓毒症相关急性肾损伤（sepsis-associated acute kidney injury, SA-AKI）患者的死亡率，其性能往往优于传统评分系统。尽管此类模型已展现出有效性，但其质量参差不齐的问题仍令人担忧。本综述旨在对现有基于机器学习的SA-AKI死亡率预测模型进行评估，重点关注模型开发质量、方法学严谨性以及预测性能。
【目的】系统评价针对SA-AKI患者的基于机器学习的死亡率风险预测模型。
【方法】本研究于PubMed、Cochrane、Embase及Web of Science四大数据库中开展针对基于机器学习的SA-AKI死亡率预测模型的全面文献检索，检索时限设置为自建库至2025年7月。由两名研究人员独立完成文献筛选、数据提取工作，并采用人工智能预测模型偏倚风险评估工具（Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence）对模型质量进行评价。
【结果】最终纳入9项研究，所有研究均包含模型开发与验证两个阶段；其中5项仅开展了内部验证，剩余4项同时完成了外部验证。纳入研究共使用了18种不同的算法，其中随机森林（Random Forest）与极端梯度提升（Extreme Gradient Boosting）为最常用的算法。多数研究采用K近邻（K-nearest neighbor）或链式方程多重插补（Multiple Imputation by Chained Equations）处理缺失值，并使用递归特征消除（Recursive Feature Elimination）、最小绝对收缩与选择算子（Least Absolute Shrinkage and Selection Operator）以及博鲁塔算法（Boruta's algorithm）开展特征选择。共有7项研究评估了模型的校准性能。训练集的曲线下面积（Area Under the Curve, AUC）普遍介于0.75至0.99之间，在内部验证阶段则降至0.70至0.87。极端梯度提升在外部验证中始终展现出稳定可靠的性能。最终纳入的预测因子涵盖六大主要类别：人口统计学信息、生命体征、实验室检验指标、疾病严重程度、合并症以及临床干预措施。
【结论】基于机器学习的模型在预测SA-AKI患者死亡率风险方面展现出良好的性能与应用潜力，且核心预测因子具有一致性。然而，多数研究存在潜在的偏倚风险。未来研究应着力提升数据处理、特征选择与验证流程的标准化水平。此外，还需聚焦于基于早期变量的前瞻性模型构建，并确保模型的可解释性与临床整合性，以推动其在临床诊疗流程中的实际应用。
【系统综述注册编号】：CRD42025634551